Enhancement of MS signal processing toward improved cancer biomarker discovery

增强 MS 信号处理以改善癌症生物标志物的发现

• 批准号: 7291560
• 负责人: Dariya I. Malyarenko
• 金额: $ 46.66万
• 依托单位: COLLEGE OF WILLIAM AND MARY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-29 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7291560
• 关键词: Address; Algorithms; Applied Research; Biochemical; Bioinformatics; Biological Markers; Biomedical Research; Body Fluids; Cancer Detection; Charge; Clinical; Collaborations; Computational algorithm; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Diagnostic Neoplasm Staging; Effectiveness; Facility Construction Funding Category; Goals; Health; Heating; Human; Imagery; Ions; Label; Least-Squares Analysis; Liquid Chromatography; Location; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measures; Molecular; Molecular Profiling; Noise; Parents; Patients; Pattern; Physics; Positioning Attribute; Protein Databases; Proteins; Proteomics; Range; Research; Research Personnel; Resolution; Sampling; Scanning; Scientist; Screening procedure; Signal Transduction; Site; Software Engineering; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Standards of Weights and Measures; Statistical sensitivity; Surveys; Survival Rate; Techniques; Technology; Testing; Time; Translating; Uncertainty; Validation; Virginia; Work; adduct; base; college; comparative; computerized data processing; computerized tools; cost; improved; ionization; leukemia; medical schools; outcome forecast; programs; protein purification; reconstruction; research study; time use; tool; tumor molecular fingerprint

DESCRIPTION (provided by applicant): The comprehensive and quantitative analysis of clinical proteomic samples is an outstanding challenge in biomedical research. New proteomic technologies for cancer detection are urgently needed and hold great potential for improving human health, as underscored by the improved survival rates of patients diagnosed in he early stages of cancer. To this end, we will develop computational tools aimed at increasing the effectiveness of cancer biomarker discovery from label-free MALDI-TOF (matrix-assisted laser- desorption/ionization time-of-flight) mass spectra for verification and identification. The computational algorithms and tools will result in more than an order of magnitude increase in both sensitivity and selectivity For molecular biomarker screening. Specifically, we propose: (i) to optimize signal processing resulting in at east a 4-fold enhancement of sensitivity (as measured by signal-to-noise), 2-fold gain in selectivity (resolution), and 10-fold increase in mass accuracy (Aim 1); (ii) to automate detection of ionization satellite ons followed by mass recalibration (Aim 2) resulting in tripling selectivity and mass accuracy; (iii) to deconvolve intensity distributions from satellite ions into parent protein peaks (Aim 3) resulting in tripling sensitivity for statistical detection and experimental identification of biomarkers from enhanced molecular maps (Aim 4). The increased efficiency of broad mass range screening will decrease the time and cost of the downstream identification and validation experiments. The successful completion of the studies described in this application will provide a basis for expanding these computational tools to other TOP MS platforms, and advance the endeavor of characterizing molecular basis for cancer toward better prognosis and treatment strategies.

描述（由申请人提供）： 临床蛋白质组学样品的全面和定量分析是生物医学研究中的一项挑战。迫切需要新的用于癌症检测的蛋白质组学技术，并具有改善人类健康的巨大潜力，这是由于在癌症早期诊断出的患者的生存率提高所强调。为此，我们将开发旨在从无标签的MALDI-TOF（基质辅助激光解吸/电离飞行时间）质谱从无标签的MALDI-TOF中提高癌症生物标志物发现的有效性的计算工具。计算算法和工具将导致分子生物标志物筛选的灵敏度和选择性的数量级提高。具体而言，我们建议：（i）优化在East处导致的信号处理，提高了4倍的灵敏度（通过信号到噪声来衡量），选择性（分辨率）增长2倍（分辨率），质量精度提高了10倍（AIM 1）； （ii）自动检测电离卫星的自动化，然后进行质量重新校准（AIM 2），导致选择性和质量精度的三倍； （iii）从卫星离子到父蛋白峰的反vlove强度分布（AIM 3），导致对增强分子图的生物标志物的统计检测和实验鉴定的敏感性三倍（AIM 4）。广泛质量筛查的效率提高将降低下游识别和验证实验的时间和成本。在本申请中描述的研究的成功完成将为将这些计算工具扩展到其他顶级MS平台提供基础，并促进表征癌症分子基础的努力，以朝着更好的预后和治疗策略。